Skip to main content

Advertisement

SpringerLink
Log in

Mesh Router Nodes Placement for Wireless Mesh Networks Based on an Enhanced Moth–Flame Optimization Algorithm

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

This paper proposes an enhanced version of Moth Flame Optimization (MFO) algorithm, called Enhanced Chaotic Lévy Opposition-based MFO (ECLO-MFO) for solving the mesh router nodes placement problem in wireless mesh network (WMN-MRNP). The proposed ECLO-MFO incorporates three strategies including the chaotic map concept, the Lévy flight strategy, and the Opposition-Based Learning (OBL) technique to enhance the optimization performance of MFO. Firstly, chaotic maps are used to increase the chaotic stochastic behavior of the MFO algorithm. Lévy flight distribution is adopted to increase the population diversity of MFO. Finally, OBL is introduced to improve the convergence speed of MFO and to explore the search space effectively. The effectiveness of the proposed ECLO-MFO is tested based on various scenarios under different settings, considering network connectivity and client coverage metrics. The results of simulation obtained using MATLAB 2020a demonstrate the accuracy and superiority of ECLO-MFO in determining the optimal positions of mesh routers when compared with the original MFO and ten other optimization algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), Harmony Search (HS), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CS), Bat Algorithm (BA), Firefly optimization (FA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Algorithm 2
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29

Similar content being viewed by others

References

  1. Akyildiz If, Wang X (2005) A survey on wireless mesh networks. IEEE Commun Mag 43 (9):S23–S30

    Article  Google Scholar 

  2. Karthika KC (2016) Wireless mesh network: a survey. In: 2016 international conference on wireless communications, signal processing and networking (WiSPNET). IEEE, pp 1966–1970

  3. Rao NA, Babu PR, Reddy AR (2021) Analysis of wireless mesh networks in machine learning approaches. In: Proceedings of international conference on advances in computer engineering and communication systems. Springer, pp 321–331

  4. Qiu L, Bahl P, Rao A, Zhou L (2006) Troubleshooting wireless mesh networks. ACM SIGCOMM Comput Commun Rev 36(5):17–28

    Article  Google Scholar 

  5. Amaldi Ed, Capone A, Cesana M, Filippini I, Malucelli F (2008) Optimization models and methods for planning wireless mesh networks. Comput Netw 52(11):2159–2171

    Article  Google Scholar 

  6. Taleb SM, Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A (2022) Nodes placement in wireless mesh networks using optimization approaches: a survey. Neural Comput Appl:1–37

  7. Lee G, Murray AT (2010) Maximal covering with network survivability requirements in wireless mesh networks. Comput Environ Urban Syst 34(1):49–57

    Article  Google Scholar 

  8. Shillington L, Tong D (2011) Maximizing wireless mesh network coverage. Int Reg Sci Rev 34(4):419–437

    Article  Google Scholar 

  9. Targon V, Sansò B, Capone A (2010) The joint gateway placement and spatial reuse problem in wireless mesh networks. Comput Netw 54(2):231–240

    Article  Google Scholar 

  10. Martignon F, Paris S, Capone A (2011) Optimal node placement in distributed wireless security architectures. In: International conference on research in networking. Springer, pp 319–330

  11. So A, Liang B (2009) Optimal placement and channel assignment of relay stations in heterogeneous wireless mesh networks by modified bender’s decomposition. Ad Hoc Netw 7(1):118–135

    Article  Google Scholar 

  12. Li F, Wang Y, Li X-Y, Nusairat A, Yanwei W (2008) Gateway placement for throughput optimization in wireless mesh networks. Mob Netw Appl 13(1-2):198–211

    Article  Google Scholar 

  13. Liu W, Nishiyama H, Kato N, Shimizu Y, Kumagai T (2013) A novel gateway selection technique for throughput optimization in configurable wireless mesh networks. Int J of Wirel Inf Netw 20(3):195–203

    Article  Google Scholar 

  14. Xhafa F, Sanchez C, Barolli L, Spaho E (2010) Evaluation of genetic algorithms for mesh router nodes placement in wireless mesh networks. J Ambient Intell Humanized Comput 1(4):271–282

    Article  Google Scholar 

  15. Oda T, Sakamoto S, Spaho E, Ikeda M, Xhafa F, Barolli L (2013) Performance evaluation of wmn-ga for wireless mesh networks considering mobile mesh clients. In: 2013 5th international conference on intelligent networking and collaborative systems. IEEE, pp 77–84

  16. Xhafa F, Sánchez C, Barolli L (2012) Local search methods for efficient router nodes placement in wireless mesh networks. J Intell Manuf 23(4):1293–1303

    Article  Google Scholar 

  17. Hirata A, Oda T, Saito N, Nagai Y, Toyoshima K, Barolli L (2021) A ccm-based hc system for mesh router placement optimization: a comparison study for different instances considering normal and uniform distributions of mesh clients. In: International conference on network-based information systems pages. Springer, pp 329–340

  18. Xhafa F, Barolli A, Sánchez C, Barolli L (2011) A simulated annealing algorithm for router nodes placement problem in wireless mesh networks. Simul Model Pract Theory 19(10):2276–2284

    Article  Google Scholar 

  19. Sayad L, Bouallouche-Medjkoune L, Aissani D (2018) A simulated annealing algorithm for the placement of dynamic mesh routers in a wireless mesh network with mobile clients. Internet Technol Lett 1(5):e35

    Article  Google Scholar 

  20. Xhafa F, Sánchez C, Barolli A, Takizawa M (2015) Solving mesh router nodes placement problem in wireless mesh networks by tabu search algorithm. J Comput Syst Sci 81(8):1417–1428

    Article  MathSciNet  Google Scholar 

  21. Zhang H, Wu S, Zhang C, Krishnamoorthy S (2021) Optimal distribution in wireless mesh network with enhanced connectivity and coverage. In: Proceedings of the 9th international conference on computer engineering and networks. Springer pp 117–1128

  22. Le TV, Huu Dinh N, Nguyen NG (2011) A novel pso-based algorithm for gateway placement in wireless mesh networks. In: 2011 IEEE 3rd International Conference on Communication Software and networks. IEEE, pp 41–45

  23. Lin C-C (2013) Dynamic router node placement in wireless mesh networks: a pso approach with constriction coefficient and its convergence analysis. Inf Sci 232:294–308

    Article  MathSciNet  Google Scholar 

  24. Wang W (2020) Deployment and optimization of wireless network node deployment and optimization in smart cities. Comput Commun 155:117–124

    Article  Google Scholar 

  25. Barolli A, Bylykbashi K, Qafzezi E, Sakamoto S, Barolli L, Takizawa M (2021) A comparison study of chi-square and uniform distributions of mesh clients for different router replacement methods using wmn-psodga hybrid intelligent simulation system. J High Speed Netw (Preprint):1–16

  26. Sakamoto S, Ozera K, Barolli A, Ikeda M, Barolli L, Takizawa M (2019) Implementation of an intelligent hybrid simulation systems for wmns based on particle swarm optimization and simulated annealing: performance evaluation for different replacement methods. Soft Comput 23(9):3029–3035

    Article  Google Scholar 

  27. Sakamoto S, Liu Y, Barolli L, Okamoto S (2021) Performance evaluation of cm and riwm router replacement methods for wmns by wmn-psohc hybrid intelligent simulation system considering chi-square distribution of mesh clients. In: International conference on innovative mobile and internet services in ubiquitous computing. Springer, pp 179–187

  28. Taleb SM, Meraihi Y, Gabis AB, Mirjalili S, Zaguia A, Ramdane-Cherif A (2022) Solving the mesh router nodes placement in wireless mesh networks using coyote optimization algorithm. IEEE Access

  29. Katayama K (2020) A coverage construction method based hill climbing approach for mesh router placement optimization. In: Advances on broad-band wireless computing, communication and applications: proceedings of the 15th international conference on broad-band and wireless computing, communication and applications (BWCCA-2020), vol 159. Springer Nature, p 355

  30. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  31. Prasanthi A, Shareef H, Errouissi R, Asna M, Wahyudie A (2021) Quantum chaotic butterfly optimization algorithm with ranking strategy for constrained optimization problems. IEEE Access 9:114587–114608

    Article  Google Scholar 

  32. Mirjalili S (2015) Moth-flame optimization algorithm; a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Article  Google Scholar 

  33. Trivedi IN, Kumar A, Ranpariya AH, Jangir P (2016) Economic load dispatch problem with ramp rate limits prohibited operating zones solve using levy flight moth-flame optimizer. In: 2016 international conference on energy efficient technologies for sustainability (ICEETS). IEEE, pp 442–447

  34. Mei RNS, Sulaiman MH, Mustaffa Z, Daniyal H (2017) Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique. Appl Soft Comput 59:210–222

    Article  Google Scholar 

  35. Elsakaan AA, El-Sehiemy RA-A, Kaddah SS, Elsaid MI (2018) Economic power dispatch with emission constraint and valve point loading effect using moth flame optimization algorithm. In: Advanced Engineering Forum. Trans Tech Publ vol 28, pp 139–149

  36. Singh P, Prakash S (2017) Optical network unit placement in fiber-wireless (fiwi) access network by moth-flame optimization algorithm. Opt Fiber Technol 36:403–411

    Article  Google Scholar 

  37. Sapre S, Mini S (2020) Moth flame optimization algorithm based on decomposition for placement of relay nodes in wsns. Wirel Netw 26(2):1473–1492

    Article  Google Scholar 

  38. Zhou Y, Yang X, Ling Y, Zhang J (2018) Meta-heuristic moth swarm algorithm for multilevel thresholding image segmentation. Multimed Tools Appl 77(18):23699–23727

    Article  Google Scholar 

  39. Raju M, Saikia LC, Saha D (2016) Automatic generation control in competitive market conditions with moth-flame optimization based cascade controller. In: 2016 IEEE region 10 conference (TENCON). IEEE, pp 734–738

  40. Yousri DA, AbdelAty AM, Said LA, AboBakr A, Radwan AG (2017) Biological inspired optimization algorithms for cole-impedance parameters identification. AEU-Int J Electron Commun 78:79–89

    Article  Google Scholar 

  41. Trivedi IN, Jangir P, Parmar SA, Jangir N (2018) Optimal power flow with voltage stability improvement and loss reduction in power system using moth-flame optimizer. Neural Comput Appl 30(6):1889–1904

    Article  Google Scholar 

  42. Huang LN, Yang B, Zhang XS, Yin LF, Yu T, Fang ZH (2019) Optimal power tracking of doubly fed induction generator-based wind turbine using swarm moth–flame optimizer. Trans Inst Meas Control 41(6):1491–1503

    Article  Google Scholar 

  43. Acharyulu BVS, Mohanty B, Hota PK (2019) Comparative performance analysis of pid controller with filter for automatic generation control with moth-flame optimization algorithm. In: Applications of artificial intelligence techniques in engineering. Springer, pp 509–518

  44. Ewees AA, Sahlol AT, Mohamed AA (2017) A bio-inspired moth-flame optimization algorithm for arabic handwritten letter recognition. In: International conference on control artificial intelligence robotics & optimization (ICCAIRO). IEEE, pp 154–159

  45. Soliman GM, Khorshid MM, Abou-El-Enien TH (2016) Modified moth-flame optimization algorithms for terrorism prediction. Int J Appl Innov Eng Manag 5(7):47–58

    Google Scholar 

  46. Naidu K, Mokhlis H, Abu Bakar AH (2014) Multiobjective optimization using weighted sum artificial bee colony algorithm for load frequency control. Int J of Electr Power Energy Syst 55:657–667

    Article  Google Scholar 

  47. Marler TR, Arora JS (2010) The weighted sum method for multi-objective optimization: new insights. Struct Multidiscip Optim 41(6):853–862

    Article  MathSciNet  Google Scholar 

  48. Chechkin AV, Metzler R, Klafter J, Gonchar VY et al (2008) Introduction to the theory of lévy flights. Anomalous Transport, 129

  49. Meraihi Yassine, Acheli Dalila, Ramdane-Cherif Amar (2019) Qos multicast routing for wireless mesh network based on a modified binary bat algorithm. Neural Comput Appl 31(7):3057– 3073

    Article  Google Scholar 

  50. Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25(5):1077–1097

    Article  Google Scholar 

  51. Mansouri A, Wang X (2020) A novel one-dimensional sine powered chaotic map and its application in a new image encryption scheme. Inf Sci 520:46–62

    Article  MathSciNet  Google Scholar 

  52. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06), vol 1. IEEE, pp 695–701

  53. Oda T, Elmazi D, Barolli A, Sakamoto S, Barolli L, Xhafa F (2016) A genetic algorithm-based system for wireless mesh networks: analysis of system data considering different routing protocols and architectures. Soft Comput 20(7):2627–2640

    Article  Google Scholar 

  54. Yang X-S (2009) Harmony search as a metaheuristic algorithm. In: Music-inspired harmony search algorithm. Springer, pp 1–14

  55. Lin C-C, Tseng P-T, Wu T-Y, Deng D-J (2016) Social-aware dynamic router node placement in wireless mesh networks. Wirel Netw 22(4):1235–1250

    Article  Google Scholar 

  56. Karaboga D (2010) Artificial bee colony algorithm. Scholarpedia 5(3):6915

    Article  Google Scholar 

  57. Lin C-C, Li Y-S, Deng D-J (2014) A bat-inspired algorithm for router node placement with weighted clients in wireless mesh networks. In: 9th international conference on communications and networking in China. IEEE, pp 139–143

  58. Yang X-S, Deb S (2009) Cuckoo search via lévy flights. In: 2009 world congress on nature & biologically inspired computing (naBIC). IEEE, pp 210–214

  59. Sayad L, Aissani D, Bouallouche-Medjkoune L (2018) Placement optimization of wireless mesh routers using firefly optimization algorithm. In: International Conference on Smart Communications in Network Technologies (saconet). IEEE, pp 144–148

  60. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  61. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LIST Laboratory, University of M’Hamed Bougara Boumerdes, Avenue of Independence, 35000, Boumerdes, Algeria

    Sylia Mekhmoukh Taleb & Yassine Meraihi

  2. Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, 4006 QLD, Australia

    Seyedali Mirjalili

  3. Yonsei Frontier Lab, Yonsei University, Seoul, Korea

    Seyedali Mirjalili

  4. LAA Laboratory, University of M’Hamed Bougara Boumerdes, Avenue of Independence, 35000, Boumerdes, Algeria

    Dalila Acheli

  5. LISV Laboratory, University of Versailles St-Quentin-en-Yvelines, 10-12 Avenue of Europe, 78140, Velizy, France

    Amar Ramdane-Cherif

  6. Ecole nationale Supérieure d’Informatique, Laboratoire des Méthodes de Conception des Systèmes, BP 68M, 16309, Oued-Smar, Alger, Algeria

    Asma Benmessaoud Gabis

Authors
  1. Sylia Mekhmoukh Taleb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yassine Meraihi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seyedali Mirjalili
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dalila Acheli
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amar Ramdane-Cherif
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Asma Benmessaoud Gabis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sylia Mekhmoukh Taleb.

Ethics declarations

Conflict of Interests

The authors declare that there is no conflict of interest with any person(s) or Organization(s).

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Taleb, S.M., Meraihi, Y., Mirjalili, S. et al. Mesh Router Nodes Placement for Wireless Mesh Networks Based on an Enhanced Moth–Flame Optimization Algorithm. Mobile Netw Appl 28, 518–541 (2023). https://doi.org/10.1007/s11036-022-02059-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-022-02059-6

Keywords

Search

Navigation